Output-Constrained Bayesian Neural Networks

Abstract

Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space. We formulate a prior that incorporates functional constraints about what the output can or cannot be in regions of the input space. Output-Constrained BNNs (OC-BNN) represent an interpretable approach of enforcing a range of constraints, fully consistent with the Bayesian framework and amenable to black-box inference. We demonstrate how OC-BNNs improve model robustness and prevent the prediction of infeasible outputs in two real-world applications of healthcare and robotics.

Presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning and Workshop on Understanding and Improving Generalization in Deep Learning. Long Beach, CA, 2019

References in corpus (2)

• Noise Contrastive Priors for Functional Uncertainty
• Constraining the Dynamics of Deep Probabilistic Models

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